Curriculum

The curriculum was constantly updated throughout the duration of the project, based on the process model adopted for the production of learning resources (see figure below). This model is driven by a participatory approach that defines a series of iterations in the production of learning materials, with multiple revisions from internal and external stakeholders, in order to ensure high quality in the produced materials.

Final curriculum (July 2017)

Module

Topic

Stage

Status as of July 2017

1

Foundations of Data Science

Foundations

Released and revised

2

Foundations of Big Data

Foundations

Released

3

Statistical / Mathematical Foundations

Foundations

Released

4

Programming / Computational Thinking (R and Python)

Foundations

Newly released

5

Data Management and Curation

Storage and Processing

Released

6

Big Data Architecture

Storage and Processing

Released

7

Distributed Computing

Storage and Processing

Released and revised

8

Data Intensive Computing

Storage and Processing

Newly released

9

Linked Data and the Semantic Web

Storage and Processing

Released as FutureLearn MOOC in April 2016

10

Machine Learning, Data Mining and Basic Analytics

Analysis

Released and revised

11

Big Data Analytics

Analysis

Released

12

Process Mining

Analysis

Released

13

Social Media Analytics

Analysis

Newly released

14

Data Visualisation and Storytelling

Interpretation and Use

Released

15

Data Exploitation including data markets and licensing

Interpretation and Use

Newly released

Revised curriculum (July 2016)

Module

Topic

Stage

Status as of July 2016

1

Foundations of Data Science

Foundations

Released and revised

2

Foundations of Big Data

Foundations

Released

3

Statistical / Mathematical Foundations

Foundations

Newly released

4

Programming / Computational Thinking (R and Python)

Foundations

To be released in July 2017

5

Data Management and Curation

Storage and Processing

Newly released

6

Big Data Architecture

Storage and Processing

Released

7

Distributed Computing

Storage and Processing

Released and revised

8

Stream Processing

Storage and Processing

To be released in July 2017

9

Linked Data and the Semantic Web

Storage and Processing

Released as FutureLearn MOOC in April 2016

10

Machine Learning, Data Mining and Basic Analytics

Analysis

Released and Revised

11

Big Data Analytics

Analysis

Newly released

12

Process Mining

Analysis

Released

13

Social Media Analytics

Analysis

To be released in July 2017

14

Data Visualisation and Storytelling

Interpretation and Use

Newly released

15

Data Exploitation including data markets and licensing

Interpretation and Use

To be released in July 2017

Initial curriculum (July 2015)

Module

Topic

1

Foundations of Data Science

2

Foundations of Big Data

3

Statistical / Mathematical Foundations

4

Programming / Computational Thinking (R and Python)

5

Data Management and Curation

6

Big Data Architecture

7

Distributed Computing

8

Stream Processing

9

Machine Learning, Data Mining and Basic Analytics

10

Big Data Analytics

11

Process Mining

12

Data Visualisation

13

Visual Analytics

14

Finding Stories in Open Data

15

Data Exploitation including data markets and licensing

What do you think about our curriculum? Are there any modules you are particularly interested in? Please leave your feedback in the comments section below.

5 Comments

Hi dears at EDSA,
You people are doing a great work.
My query is related to the existing curriculum that is published on your site(http://edsa-project.eu/resources/curriculum/). Is it correct to say that the learning sequence of the new entrant in this field should be the same as the order of Modules (1 to 15)?
I am an experienced telecom engineer(15 plus years in wireless GSM/UMTS operators and vendor) and now am gearing toward learning Data Sciences as this is the future of technical maintenance and support related activities.
Cheers, Asad